Evidence map›Paper›PMID 27053248›Full record

ReviewClinical and translational medicine2016

Genomics, microRNA, epigenetics, and proteomics for future diagnosis, treatment and monitoring response in upper GI cancers.

Björn L D M Brücher, Yan Li, Philipp Schnabel, Martin Daumer, Timothy J Wallace, Rainer Kube, Bruno Zilberstein, Scott Steele, Jan L A Voskuil, Ijaz S Jamall

Open access · goldAbstract readReview
In one paragraph

Review in Clinical and translational medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.0field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Review
  4. Big Data in Gastroenterology Research.International journal of molecular sciences · 2023
    Review
  5. Review
  6. Article
  7. High-Throughput Sequencing Strategy for miR-146b-regulated circRNA Expression in Hepatic Stellate Cells.Medical science monitor : international medical journal of experimental and clinical research · 2018
    Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 7 institutions in 3 countries.

Björn L D M BrücherTheodor-Billroth-Academy®, Munich, Germany. b-bruecher@gmx.de.
Yan LiProteogenomics Research Institute for Systems Medicine, San Diego, CA, USA.
Philipp SchnabelInstitute of Pathology, University of Homburg Saar, Homburg, Germany.
Martin DaumerTheodor-Billroth-Academy®, Munich, Germany.
Timothy J WallaceBon Secours Cancer Institute, Richmond, VA, USA.
Rainer KubeDepartment of Surgery, Carl-Thiem-Klinikum, Cottbus, Germany.
Bruno ZilbersteinDepartment of Surgery, University of Sao Paulo, São Paulo, Brazil.
Scott SteeleCase Western Reserve University, Cleveland, OH, USA.
Jan L A VoskuilEverest Biotech Ltd., Upper Heyford, Oxford, UK.
Ijaz S JamallTheodor-Billroth-Academy®, Munich, Germany.
Carl-Thiem-Klinikum Cottbus · DEMadigan Army Medical Center · USProteogenomics Research Institute for Systems Medicine · USSaarland University · DESylvia Lawry Centre for Multiple Sclerosis Research · DEUniversidade de São Paulo · BRVirginia Cancer Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One major objective for our evolving understanding in the treatment of cancers will be to address how a combination of diagnosis and treatment strategies can be used to integrate patient and tumor variables with an outcome-oriented approach. Such an approach, in a multimodal therapy setting, could identify those patients (1) who should undergo a defined treatment (personalized therapy) (2) in whom modifications of the multimodal therapy due to observed responses might lead to an improvement of the response and/or prognosis (individualized therapy), (3) who might not benefit from a particular toxic treatment regimen, and (4) who could be identified early on and thereby be spared the morbidity associated with such treatments. These strategies could lead in the direction of precision medicine and there is hope of integrating translational molecular data to improve cancer classifications. In order to achieve these goals, it is necessary to understand the key issues in different aspects of biotechnology to anticipate future directions of personalized and individualized diagnosis and multimodal treatment strategies. Providing an overview of translational data in cancers proved to be a challenge as different methods and techniques used to obtain molecular data are used and studies are based on different tumor entities with different tumor biology and prognoses as well as vastly different therapeutic approaches. The pros and cons of the available methodologies and the potential response data in genomics, microRNA, epigenetics and proteomics with a focus on upper gastrointestinal cancers are considered herein to allow for an understanding of where these technologies stand with respect to cancer diagnosis, prognosis and treatment.

Indexed as

Barrett carcinomaEpigeneticsEsophageal adenocarcinomaEsophageal carcinomaEsophageal squamous cell carcinomaGenomicsMicroRNAMultimodal therapyNeoadjuvant therapyPrognosisProteomicsResponseSurvivalUpper gastrointestinal tract

Identifiers

PMID27053248
PMCPMC4823224
OpenAlexW2330532313

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.